Event:

Understanding how flexible and robust behavior emerges from slowly learned cortical representations and fast task learning remains a central challenge in neuroscience. Consider goal-directed navigation: we quickly adjust trajectories to avoid obstacles (ms-sec), rethink routes given blocked paths (min-days), and adapt habits - like biking - over seasons. While seemingly effortless, this adaptability involves vast numbers of decisions at multiple spatiotemporal scales, coordinated across distributed brain circuits. I will present past and ongoing work modeling cortical computations to explain the multiscale organization of behavior. First, I will show how recurrent dynamics in spiking models can preserve global behavioral stability even as neural response properties drift over time - a phenomenon known as representational drift. Second, I will present ongoing work studying how abstract cortical world models may emerge from the brain’s need to efficiently plan extended movement sequences, and how this could provide new opportunities to understand cognitive flexibility in embodied settings.